Firm-level Inefficiency in Post-transition Economies

Firms in post-transition economies are frequently considered less efficient than those in more advanced market economies. By relying on the World Bank Enterprise Survey for the year 2019, firm-level technical inefficiency is estimated by the stochastic frontier analysis method for a sample of European post-transition countries. To be precise, the analysis included Albania, Bosnia and Herzegovina, Croatia, Czechia, Estonia, North Macedonia, Poland, Serbia, and Slovenia. Furthermore, the factors contributing to the firm-level inefficiency are explored in a comparative setting. The effects of the international orientation of the firm, foreign ownership, doing business with the government sector, presence of informal competitors, innovation activity, manager experience, and the age of the firm on the technical inefficiency are estimated. Results show that although some factors are common to a subsample of countries, not a single factor is significant in all the analysed economies. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Level and determinants of county health system technical efficiency in Kenya: two stage data envelopment analysis

Background Improving health system efficiency is a key strategy to increase health system performance and accelerate progress towards Universal Health Coverage. In 2013, Kenya transitioned into a devolved system of government granting county governments autonomy over budgets and priorities. We assessed the level and determinants of technical efficiency of the 47 county health systems in Kenya. Methods We carried out a two-stage data envelopment analysis (DEA) using Simar and Wilson’s double bootstrap method using data from all the 47 counties in Kenya. In the first stage, we derived the bootstrapped DEA scores using an output orientation. We used three input variables (Public county health expenditure, Private county health expenditure, number of healthcare facilities), and one outcome variable (Disability Adjusted Life Years) using 2018 data. In the second stage, the bias corrected technical inefficiency scores were regressed against 14 exogenous factors using a bootstrapped truncated regression. Results The mean bias-corrected technical efficiency score of the 47 counties was 69.72% (95% CI 66.41–73.01%), indicating that on average, county health systems could increase their outputs by 30.28% at the same level of inputs. County technical efficiency scores ranged from 42.69% (95% CI 38.11–45.26%) to 91.99% (95% CI 83.78–98.95%). Higher HIV prevalence was associated with greater technical inefficiency of county health systems, while higher population density, county absorption of development budgets, and quality of care provided by healthcare facilities were associated with lower county health system inefficiency. Conclusions The findings from this analysis highlight the need for county health departments to consider ways to improve the efficiency of county health systems. Approaches could include prioritizing resources to interventions that will reduce high chronic disease burden, filling structural quality gaps, implementing interventions to improve process quality, identifying the challenges to absorption rates and reforming public finance management systems to enhance their efficiency.

Meta-regression Analysis of Technical (In)Efficiency in Agriculture: a Regional Approach

While scientific studies have not reached a consensus on the methodology for examining Technical Efficiency (or Inefficiency), the influence of regions appears to be important for efficiency scores. Therefore, this research aims to investigate the empirical procedures for the achievement of more robust results in the analysis of productive efficiency, as well as to evaluate the effect of the location of farms on such efficiency. The goal was to check whether the most developed regions are the most efficient. Meta-regression analysis provides an adequate method for an accurate assessment of both situations. This technique was applied based on a database of 166 observations on the agricultural sector from countries around the world, published in the period 2010–2017. The criteria used for the database collection and for the conceived model were not previously used and, thereby, enrich the discussion on the topic. The procedure aims to check the variation in the Mean of Technical Inefficiency and conduct an analysis using Quasi-Maximum Likelihood Estimation. The regressions showed that the Mean of Technical Inefficiency could be mainly explained by data, variables, employed empirical models and the region of study. The studies that focus on farms of developed countries present the lowest Mean of Technical Inefficiency, while studies for developing or low-income countries exhibit the opposite. Therefore, for future research on productive analysis, we suggest empirical procedures aimed at achieving robust results that take into account specific regional characteristics of farms.

An Analysis of Technical Efficiency of Vegetables’ Household Production in Mongolia

Vegetable production is one important agricultural product in crop production after wheat and potatoes production in Mongolia. Currently, household production dominates in total vegetable production (approximately 80 percent). Thus, the purposes of this paper were to measure technical efficiency and to determine influencing factors inefficiency on vegetable household production in Mongolia by using Stochastic production frontier analysis (SFA). Primary data was collected from randomly selected 260 vegetable households of Mongolia in 2019. The empirical result indicated that the average technical efficiency of the sampled vegetable household was 64.6 % (range between 43.2% and 99.9%) or they lost about 35.4% of the potential output due to technical inefficiency. We found that land and labor are the main influencing input factors of the household’s vegetable production. Also, the result of the technical inefficiency model, variables of age, sex, experience, and credit use obtained a negative relationship with inefficiency. The other variables are family size, education level, land fragmentation index was positively affected by technical inefficiency.

The Empirical Analysis of Technical Efficiency on Large Scale Grain Production in North West Ethiopia: A Stochastic Frontier Approach

This study is intended to scrutinize the technical efficiency of large scale grain crop producers in North West Ethiopia. Moreover, the study endeavored to determine socioeconomic characteristics, and farm management practices which influence the technical inefficiency in large scale grain production. Multiple stages random sampling technique was used to select 200 producers. The empirical results revealed that capital, labor, land, and seed input affect the output positively. The responsiveness of yield shows that a one-percent increase in utilization of capital, labor and land inputs will increase the grain production by 0.18%, 0.23% and 0.56% respectively, while, the agrochemical input influences the output negatively. Evidences from the technical inefficiency model shows that gender and levels of education variables increase technical efficiency. Age, occupation, district, and subsidies variables increase technical inefficiency. Producers’ technical efficiencies range from 23 to 100% and the mean technical efficiency is 71.7%. More effort should be made for adult and continuing education. Female producers should be given an opportunity in the management place in the large scale grain farming segment. In addition, the government is compelled to give strategies about proper input appliance and set up the pilot research institution in the study area.

A Stochastic Frontier Approach Applied to Farms to Selected Andean Countries

In this paper, we estimate a stochastic production function for Bolivia, Ecuador, Colombia, and Peru to investigate whether road infrastructure affects farm technical inefficiency. We use agricultural censuses of Colombia and Bolivia in 2013 and 2014, respectively; national agricultural surveys in 2017 of both Ecuador and Peru; and data on the road network and travel time to the nearest town with 50,000 inhabitants or more. Our main findings are that irrigation increases the value of production and road network decreases farm technical inefficiency, that is, road density (travel time) increases (decreases) farm technical efficiency.

Technical inefficiency of the manufacturing sector in Laos: a case study of the firm survey

PurposeThis study aims to unlock the path of growth for sustainable economic development and accomplish the government's vision 2030 by ameliorating the productivity of the manufacturing sector in Laos.Design/methodology/approachThis study applied cross-sectional data of 2,009 firms from the national firm survey, namely the Economic Census Survey (ECS), in 2012/13 in addition to employing the stochastic frontier analysis (SFA) to assess the production frontier and factors behind the technical inefficiency to arrive at policy recommendations.FindingsThe study found that the efficiency level varied across subindustries with an average of 72.51% in full potential production. Out of the five classified groups, Sub4 (chemical and plastic) was found to be the most efficient manufacturer, while the rest in order are Sub1 (food and beverage), Sub5 (furniture and others), Sub2 (garment and textile), and Sub3 (paper and printing), providing the evidence to improve the technical efficiency. This study discovered that the firm's size, accounting system and credit access are crucial to enhancing the production efficiency of all sampling firms. However, these factors might be subject to specific industries.Practical implicationsFor the implication to the business community and policymakers, the findings of this study could be a reference in terms of which areas they should concentrate on to improve the technical efficiency as a part of productivity in the manufacturing industry. For instance, it suggests that firms could improve their production efficiency by introducing the accounting system, laborers' skills (education of managers) and engaging in international trade activities. Additionally, it asks policymakers to help private firms by improving the infrastructure, credit access, training and trade facilitation.Originality/valueIt is believed that, as the major contribution in Lao literature, this study is the first research applying the largest data from the national survey – the Lao ECS – examining the technical efficiency in the manufacturing sector in the country, and overcoming the gap of the previous research which recruited few policy variables and applied a small sample size in one specific industry. Therefore, the findings of this study impart more insights into the analysis, providing more effective and credible recommendations to policymakers and firms to improve their technical efficiency and, consequently, their competitiveness.

Analysis of Factors Affecting the Technical Inefficiency on Indonesian Palm Oil Plantation

This study aims to analyze the factors affecting the technical inefficiency of palm oil plantations in Indonesia by using the stochastic frontier analysis based on the translog production function. The data used in this study are taken from the Central Statistics Agency (Agricultural Business Household Income Survey) in 2013. The number of samples used was 14367 farmers. The results revealed that there is still to increase in the efficiency of palm oil plantations in Indonesia. The production function suggests that increasing the number of trees can help to increase the number of outputs. Additionally, education, age, planting system, seed quality, extension service, and plasma farmer significantly influence the technical efficiency of palm oil plantation.

Agricultural Total Factor Productivity and Road Infrastructure in South American Countries

In this working paper, we estimate agricultural total factor productivity (Ag TFP) for South American countries over the period 19692016 and identify how road density affect technical efficiency. In 2015, Colombia, Peru, Venezuela, Ecuador, and Bolivia, the Andean countries, had 205,000; 166,000; 96,000; 89,000; and 43,000 kilometers of roads, respectively. A poor-quality and limited road network, along with inaccessibility to markets, might limit the ability of farms to efficiently manage production inputs, raising technical inefficiency. We find that the Ag TFP growth rate per year for South American countries, on average, is 1.5%. For the Andean countries, we find an even smaller growth rate per year of 1.4% on average. Our findings suggest that higher road density is associated with lower technical inefficiency.